Search Term Cleanup

PREREQUISITE: Read ../_mureo-shared/SKILL.md for auth, security rules, output format, and Tool Selection (Read/Write on Code, mureo_strategy_* / mureo_state_* MCP on Desktop / Cowork).

Tool names below refer to the built-in platforms; a namespaced variant (see _mureo-shared → Namespaced built-in variants) is used with its prefix substituted.

Review and clean up search terms and keywords across all platforms.

Prerequisites

• STRATEGY.md and STATE.json must exist (run the onboard skill first)

Steps

Before you start: Run the Diagnostic preamble from ../_mureo-shared/SKILL.md — load learning insights (mureo_learning_insights_get) and consult advisors (mureo_consult_advisor) before drawing conclusions.

0. Establish today: call mureo_state_get first, on every host (including Claude Code, where you would otherwise Read the file) and take server_now from its response — ISO 8601 with UTC offset, e.g. 2026-07-28T10:12:33+09:00. Its date is the only source of the current date for this run: the observation_due you write in step 11, and every "is a previous action still inside its observation window?" check in step 9, are measured from it. Do not shell out (this skill must run in Bash-less headless hosts) and do not read the date off STATE.json — last_synced_at, reports.*.period and action_log timestamps are history, never evidence of what day it is now. Never write server_now into STATE.json: it is a response field, and a persisted copy becomes tomorrow's stale "today".

1. Load context: Read STRATEGY.md (Persona, USP, Target Audience, Data Sources) and STATE.json (the same mureo_state_get response from step 0 on MCP hosts).

2. Discover platforms: Identify all configured platforms that support search term data from STATE.json platforms. Also include any hosted official-MCP connector present in the session (e.g. TikTok, key tiktok_ads) where it exposes search-term data — drive it via its own tools and skip mureo-only value-adds; see ../_mureo-shared/SKILL.md → Hosted-connector platforms.

3. Review search terms: For each ad platform that supports search term data:

   • Google Ads: prefer mureo native — call google_ads_search_terms_report for the raw query rows, then google_ads_search_terms_review (rule-based scoring) and google_ads_search_terms_analyze (intent classification) per campaign. These tools work in both Live API and BYOD mode. In BYOD they read from ~/.mureo/byod/google_ads/search_terms.csv (the Apps Script bundle output) — do not look for raw CSVs in the project directory; mureo BYOD data is centralized in the workspace byod/ directory (or ~/.mureo/byod/ for legacy CLI users) and is only accessible through mureo MCP tools. If mureo's Google Ads tools are unavailable (e.g. MUREO_DISABLE_GOOGLE_ADS=1 after mureo providers add google-ads-official), fall back to the official google-ads-official MCP's search-terms report tool for the raw rows, then skip the mureo-only rule-based scoring and intent-classification tools (google_ads_search_terms_review, google_ads_search_terms_analyze) and do the scoring/classification yourself using the rules described in step 6 below; note to the user that mureo's automated scoring is only available with the native MCP (install or re-enable via mureo setup claude-code).
   • Meta Ads: Skip — Meta is interest/audience-targeted and has no search-query data (this applies to both mureo native and the official Meta MCP).
   • Analyze N-gram patterns and user intent across the returned rows.

4. Paid/organic cross-reference (if Search Console is available):

   • Pull top organic queries for the site
   • Cross-reference with paid search terms to identify overlap
   • For terms ranking well organically (position 1-3), consider reducing paid bids or pausing paid keywords
   • For terms with strong paid performance but weak organic ranking, flag as SEO opportunity
   • Present a paid/organic overlap matrix

5. Landing page quality check (if GA4 is available): Check landing page performance for key search terms. Terms driving traffic to high-bounce-rate pages may need LP improvements rather than keyword changes.

6. Score candidates against strategy:

   • Exclude candidates: Terms with 0 conversions + high cost, informational-only queries, terms misaligned with Persona
   • Add candidates: High-converting terms not yet added as keywords, terms matching USP themes
   • Reduce candidates: Terms well-covered by organic rankings

   Negative keywords are a marginal decision, not an average ranking. Distinguish systematic waste — an n-gram or intent pattern that is wrong for the Persona at any volume, where a negative genuinely removes future spend — from a low-volume term with a bad average, where a handful of clicks and zero conversions is a sample-size fact, not evidence of waste. Excluding the long tail one term at a time mostly narrows match reach without saving meaningful spend. Prefer pattern-level negatives with a stated marginal saving; leave thin terms to accumulate. See ../_mureo-pro-diagnosis/SKILL.md → Allocation & Learning-State Discipline.

7. Present recommendations in a table:

   Term	Platform	Action	Reason	Score	Campaign

   Group by platform and campaign. Show estimated cost savings from exclusions.

8. Ask for approval: Let me select which recommendations to apply.

9. Check pending observations: Before executing, check action_log for this campaign. If a previous action is still within its observation window, warn that stacking changes will make outcome evaluation difficult. Recommend waiting if possible.

10. Open a batch: call mureo_batch_begin with a label naming this pass (e.g. "search-term cleanup 2026-08-07"). A cleanup changes many entities across possibly several platforms, and the batch is what makes it one reviewable, plannable unit instead of a set of entries nobody can re-identify later. See ../_mureo-shared/SKILL.md → Bulk changes are one revertible unit.

11. Execute: Use each platform's keyword management tools to apply approved changes (add negative keywords, add positive keywords, adjust bids).

12. Record outcome context: For each campaign modified, log to action_log with observation_due (14 days from server_now's date); omit metrics_at_action and mureo fills it from the campaign's snapshot. This enables evidence-based evaluation later. Keyword and negative-keyword changes are not auto-recorded, so this step is what puts them in the batch at all — an entry you do not append is invisible to any later revert.

13. Close the batch: call mureo_batch_end and report the returned batch_id and member count to the operator, so undoing this pass later is rollback_plan_get with that id rather than a reconstruction from memory.

14. Update STATE.json with notes about the cleanup.

IMPORTANT: Always explain WHY a term should be excluded/added, referencing the Persona or USP from STRATEGY.md. Consult past action_log entries — if a similar cleanup was previously evaluated, reference whether it was effective.